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import gradio as gr
from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import tensorflow as tf
from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
feature_extractor = SegformerFeatureExtractor.from_pretrained(
"nickmuchi/segformer-b4-finetuned-segments-sidewalk"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
"nickmuchi/segformer-b4-finetuned-segments-sidewalk",
from_pt=True
)
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [
[204, 87, 92],
[112, 185, 212],
[45, 189, 106],
[234, 123, 67],
[78, 56, 123],
[210, 32, 89],
[90, 180, 56],
[155, 102, 200],
[33, 147, 176],
[255, 183, 76],
[67, 123, 89],
[190, 60, 45],
[134, 112, 200],
[56, 45, 189],
[200, 56, 123],
[87, 92, 204],
[120, 56, 123],
[45, 78, 123],
[156, 200, 56],
[32, 90, 210],
[56, 123, 67],
[180, 56, 123],
[123, 67, 45],
[45, 134, 200],
[67, 56, 123],
[78, 123, 67],
[32, 210, 90],
[45, 56, 189],
[123, 56, 123],
[56, 156, 200],
[189, 56, 45],
[112, 200, 56],
[56, 123, 45],
[200, 32, 90],
[255, 255, 0],
]
labels_list = []
with open(r'labels.txt', 'r') as fp:
for line in fp:
labels_list.append(line[:-1])
colormap = np.asarray(ade_palette())
def label_to_color_image(label):
if label.ndim != 2:
raise ValueError("Expect 2-D input label")
if np.max(label) >= len(colormap):
raise ValueError("label value too large.")
return colormap[label]
def draw_plot(pred_img, seg, cursor_pos):
fig = plt.figure(figsize=(20, 15))
grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
plt.subplot(grid_spec[0])
plt.imshow(pred_img)
plt.axis('off')
LABEL_NAMES = np.asarray(labels_list)
FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
unique_labels = np.unique(seg.numpy().astype("uint8"))
ax = plt.subplot(grid_spec[1])
cursor_x, cursor_y = cursor_pos
mask = seg.numpy() == seg.numpy()[cursor_x, cursor_y]
mask_image = FULL_COLOR_MAP[mask].reshape(pred_img.shape)
plt.imshow(mask_image.astype(np.uint8), interpolation="nearest")
ax.yaxis.tick_right()
plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
plt.xticks([], [])
ax.tick_params(width=0.0, labelsize=25)
return fig
def sepia(input_img, cursor_pos):
input_img = Image.fromarray(input_img)
inputs = feature_extractor(images=input_img, return_tensors="tf")
outputs = model(**inputs)
logits = outputs.logits
logits = tf.transpose(logits, [0, 2, 3, 1])
logits = tf.image.resize(
logits, input_img.size[::-1]
)
seg = tf.math.argmax(logits, axis=-1)[0]
fig = draw_plot(np.array(input_img), seg, cursor_pos)
return fig
demo = gr.Interface(fn=sepia,
inputs=["image", "canvas"],
outputs="plot",
examples=[["side-1.jpg", [200, 300]], ["side-2.jpg", [150, 250]], ["side-3.jpg", [100, 200]], ["side-4.jpg", [250, 400]]],
live=True,
allow_flagging='never')
demo.launch() |